{"id":"W4385830559","doi":"10.1016/j.atech.2023.100300","title":"Improving the network architecture of YOLOv7 to achieve real-time grading of canola based on kernel health","year":2023,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Computer vision; Video tracking; Pattern recognition (psychology); Pooling; Pixel; Object detection; Object (grammar)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002632112,0.0007272637,0.0003059921,0.0003822513,0.0002616202,0.0005116545,0.001487826,0.0005456018,0.002706892],"category_scores_gemma":[0.0005325718,0.0003093495,0.0004726857,0.0001813519,0.0001936488,0.0006029228,0.0004603961,0.0006568196,0.001040553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009025125,"about_ca_system_score_gemma":0.0008034835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02051933,"about_ca_topic_score_gemma":0.02353681,"domain_scores_codex":[0.999898,0.000007070926,0.000004012622,0.00003936537,0.00002289787,0.00002868599],"domain_scores_gemma":[0.9998608,0.00002027017,0.00001100944,0.00001558314,0.00008003821,0.00001228644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006059633,0.0003565894,0.008017829,0.0001877958,0.0002125272,0.0002165553,0.0001169667,0.4497419,0.07742812,0.002871985,0.01334362,0.4469002],"study_design_scores_gemma":[0.00001043877,0.0001005433,0.001293209,0.000009076312,0.00003698331,0.00003113312,0.000009931771,0.9841265,0.01226712,0.0004905795,0.00161401,0.00001040264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3846158,0.001364486,0.5831505,0.0007113555,0.0004447034,0.0002549857,0.001172384,0.01411628,0.01416953],"genre_scores_gemma":[0.891561,0.0004275963,0.09371128,0.0003039039,0.00005273308,0.0001857834,0.002085664,0.0001947387,0.01147729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02051933,"threshold_uncertainty_score":0.04079974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009391540884514712,"score_gpt":0.2101810846986545,"score_spread":0.2007895438141398,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}